In this project we propose to validate a mobile device based dark adaptation measurement method for detection of age-related macular degeneration (AMD). Dark adaptation (DA) is the natural process where our eyes adapt to darkness after being exposed to a bright light. This process of DA is known to be impaired or delayed in people with AMD, even at a very early stage when other vision measures such as visual acuity (VA) are unaltered. Delays in dark adaptation are also correlated with severity of AMD. Thus, DA is a key biomarker of AMD which can be helpful in early detection and even monitoring disease progression. While dedicated clinical devices for DA measurement have been developed, their high cost and limited availability are barriers for adoption of DA testing as part of screening tests for AMD. If a key vision measurement like dark adaptation can be adopted on a consumer electronics platform such as mobile devices, it can be made accessible to a large at-risk population in a cost-effective manner. We have developed a method for mobile device based DA measurement (MOBILE-DA). Subjects sitting in a dark room fixate on a target on the mobile device placed at reading distance, and a series of visual stimuli are presented at a fixed eccentricity with respect to the fixation. The subjects respond to the perceived stimulus by tapping the screen and the device logs the data. Preliminary experimentation suggests that it is feasible to measure DA using contemporary mobile devices. The DA response measured using MOBILE-DA differed significantly between AMD subjects and elderly normal controls, indicating that MOBILE-DA could be sensitive to measuring functional vision changes occurring in AMD. In this project, our main aim is to evaluate the effectiveness of MOBILE-DA in measuring DA characteristics in early/intermediate stage AMD patients with visual acuity 20/40 or better. A significant association of AMD presence on MOBILE-DA characteristics will imply that MOBILE-DA can be potentially used as an early detection tests in clinic. We will compare MOBILE-DA measurements with an existing clinical dark adaptometer device in all study participants for its further validation. We will also compare MOBILE-DA measurements between early and intermediate AMD subject groups to determine whether MOBILE-Da parameters are significantly associated with the severity of AMD. This will impact its potential to be used for monitoring disease progression. The second aim of this project is to develop a custom smartphone virtual reality (VR) googles for deploying MOBILE-DA to obtain better control over measurement conditions by reducing the interference of external lighting conditions and for allowing automated monitoring of fixation stability during the test using the front camera of the smartphones and novel image processing algorithms. We will compare within-subjects the MOBILE-DA measurements with and without smartphone VR googles mode. This study will help us comprehensively evaluate different aspects of MOBILE-DA and its potential to impact AMD screening in the short-term, home-monitoring in AMD patients by enabling self-testing in the long term.